Identification of  Residential and Commercial Area using Convolutional Neural Network

Valliappan Raman; Putra Sumari; Prabhavathy M; Sundresan Perumal

doi:10.33102/mjosht.v10i2.396

Authors

Valliappan Raman Department of Artificial Intelligence and Data Science, Coimbatore Institute of Technology, Coimbatore, Tamilnadu 641014, India.
Putra Sumari School of Computer Science, Universiti Sains Malaysia, 11800 Penang, Malaysia.
Prabhavathy M Department of Artificial Intelligence and Data Science, Coimbatore Institute of Technology, Coimbatore, Tamilnadu 641014, India. https://orcid.org/0000-0003-2575-9024
Sundresan Perumal Faculty of Science and Technology, University Sains Islam Malaysia, 71800 Nilai, Negeri Sembilan, Malaysia.

DOI:

https://doi.org/10.33102/mjosht.v10i2.396

Keywords:

CNNs, Transfer Learning, Satellite Image

Abstract

Abstract— Image classification of land use using aerial scene classification has become increasingly common around the world. Utilizing the power of Convolutional Neural Networks (CNNs), identification of various city township areas using satellite imagery has become more efficient compared to the previous manual labeling. The objective of this research is to build a convolutional neural network model for residential and commercial area identification. In the research, we also adopted Inception V3 and VGG16 to develop two transfer learning models for the identification system. The Inception V3-based model achieved the highest overall accuracy value of 100%, showing its effectiveness in accurate residential and commercial area identification. The proposed CNN model achieved an accuracy of 99%, while the VGG-16 model with all configurations being frozen achieved 99% accuracy.

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